From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, micros...

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Autores principales:	Izzet B Yildiz, Katharina von Kriegstein, Stefan J Kiebel
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/9e29d4200cfa4238ac0d5a0c24fd5921
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spelling	oai:doaj.org-article:9e29d4200cfa4238ac0d5a0c24fd59212021-11-18T05:53:36ZFrom birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.1553-734X1553-735810.1371/journal.pcbi.1003219https://doaj.org/article/9e29d4200cfa4238ac0d5a0c24fd59212013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24068902/pdf/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.Izzet B YildizKatharina von KriegsteinStefan J KiebelPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 9, p e1003219 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Izzet B Yildiz Katharina von Kriegstein Stefan J Kiebel From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
description	Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. In addition, we show that recognition can be performed even when words are spoken by different speakers and with different accents-an everyday situation in which current state-of-the-art speech recognition models often fail. The model can also be used to qualitatively explain behavioral data on human speech learning and derive predictions for future experiments.
format	article
author	Izzet B Yildiz Katharina von Kriegstein Stefan J Kiebel
author_facet	Izzet B Yildiz Katharina von Kriegstein Stefan J Kiebel
author_sort	Izzet B Yildiz
title	From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
title_short	From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
title_full	From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
title_fullStr	From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
title_full_unstemmed	From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
title_sort	from birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/9e29d4200cfa4238ac0d5a0c24fd5921
work_keys_str_mv	AT izzetbyildiz frombirdsongtohumanspeechrecognitionbayesianinferenceonahierarchyofnonlineardynamicalsystems AT katharinavonkriegstein frombirdsongtohumanspeechrecognitionbayesianinferenceonahierarchyofnonlineardynamicalsystems AT stefanjkiebel frombirdsongtohumanspeechrecognitionbayesianinferenceonahierarchyofnonlineardynamicalsystems
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From birdsong to human speech recognition: bayesian inference on a hierarchy of nonlinear dynamical systems.

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